High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MCST-MVS		83.01 2	83.30 3	82.15 10	92.84 2	57.58 16	93.77 1	91.10 6	75.95 2	77.10 24	93.09 17	54.15 23	95.57 10	85.80 3	85.87 34	93.31 9

DELS-MVS		82.32 4	82.50 4	81.79 11	86.80 41	56.89 26	92.77 2	86.30 79	77.83 1	77.88 21	92.13 32	60.24 4	94.78 18	78.97 25	89.61 6	93.69 6
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
SED-MVS		81.92 6	81.75 7	82.44 7	89.48 14	56.89 26	92.48 3	88.94 27	57.50 202	84.61 3	94.09 4	58.81 9	96.37 5	82.28 11	87.60 16	94.06 3

OPU-MVS				81.71 12	92.05 3	55.97 42	92.48 3				94.01 6	67.21 2	95.10 13	89.82 1	92.55 3	94.06 3

DVP-MVS		81.30 8	81.00 10	82.20 8	89.40 17	57.45 18	92.34 5	89.99 14	57.71 196	81.91 9	93.64 10	55.17 17	96.44 2	81.68 13	87.13 19	92.72 21
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				82.20 8	89.50 12	57.73 13	92.34 5	88.88 29					96.39 4	81.68 13	87.13 19	92.47 24

test0726					89.40 17	57.45 18	92.32 7	88.63 37	57.71 196	83.14 7	93.96 7	55.17 17

DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 179	59.50 5	92.24 8	90.72 8	69.37 22	83.22 6	94.47 2	63.81 3	93.18 30	74.02 62	93.25 2	94.80 1

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 4	59.33 6	92.10 9	90.95 7	65.68 58	80.67 15	94.42 3	59.41 7	95.89 9	86.74 2	89.75 5	92.94 16

CNVR-MVS		81.76 7	81.90 6	81.33 15	90.04 7	57.70 14	91.71 10	88.87 30	70.31 16	77.64 23	93.87 8	52.58 30	93.91 24	84.17 4	87.92 14	92.39 26

PS-MVSNAJ		80.06 13	79.52 14	81.68 13	85.58 56	60.97 3	91.69 11	87.02 64	70.62 13	80.75 14	93.22 14	37.77 176	92.50 42	82.75 8	86.25 31	91.57 46

xiu_mvs_v2_base		79.86 14	79.31 15	81.53 14	85.03 72	60.73 4	91.65 12	86.86 67	70.30 17	80.77 13	93.07 18	37.63 181	92.28 49	82.73 9	85.71 35	91.57 46

CANet		80.90 9	81.17 9	80.09 28	87.62 34	54.21 85	91.60 13	86.47 75	73.13 5	79.89 18	93.10 15	49.88 51	92.98 31	84.09 5	84.75 47	93.08 14

lupinMVS		78.38 22	78.11 24	79.19 37	83.02 114	55.24 55	91.57 14	84.82 116	69.12 23	76.67 26	92.02 36	44.82 102	90.23 99	80.83 18	80.09 84	92.08 33

NCCC		79.57 16	79.23 16	80.59 18	89.50 12	56.99 24	91.38 15	88.17 46	67.71 37	73.81 44	92.75 21	46.88 73	93.28 28	78.79 27	84.07 53	91.50 50

test_yl		75.85 61	74.83 65	78.91 42	88.08 30	51.94 144	91.30 16	89.28 18	57.91 190	71.19 76	89.20 100	42.03 137	92.77 35	69.41 86	75.07 127	92.01 36

DCV-MVSNet		75.85 61	74.83 65	78.91 42	88.08 30	51.94 144	91.30 16	89.28 18	57.91 190	71.19 76	89.20 100	42.03 137	92.77 35	69.41 86	75.07 127	92.01 36

LFMVS		78.52 19	77.14 35	82.67 4	89.58 10	58.90 8	91.27 18	88.05 47	63.22 93	74.63 36	90.83 63	41.38 145	94.40 19	75.42 52	79.90 89	94.72 2

VDD-MVS		76.08 57	74.97 62	79.44 33	84.27 84	53.33 111	91.13 19	85.88 87	65.33 65	72.37 63	89.34 97	32.52 240	92.76 37	77.90 37	75.96 116	92.22 31

DeepPCF-MVS	69.37 1	80.65 10	81.56 8	77.94 80	85.46 62	49.56 199	90.99 20	86.66 73	70.58 14	80.07 17	95.30 1	56.18 15	90.97 78	82.57 10	86.22 32	93.28 10

VNet		77.99 30	77.92 27	78.19 71	87.43 35	50.12 189	90.93 21	91.41 4	67.48 40	75.12 32	90.15 82	46.77 75	91.00 76	73.52 67	78.46 98	93.44 7

CLD-MVS		75.60 65	75.39 56	76.24 117	80.69 170	52.40 135	90.69 22	86.20 82	74.40 3	65.01 126	88.93 104	42.05 136	90.58 88	76.57 43	73.96 133	85.73 171
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_part1		73.80 87	72.13 95	78.79 51	85.92 47	58.26 10	90.60 23	86.85 68	63.98 79	63.95 142	81.54 204	52.08 34	92.24 50	64.93 124	59.32 239	85.87 169

jason		77.01 42	76.45 44	78.69 55	79.69 182	54.74 72	90.56 24	83.99 138	68.26 28	74.10 42	90.91 60	42.14 134	89.99 103	79.30 24	79.12 92	91.36 54
jason: jason.
IB-MVS	68.87 2	74.01 83	72.03 100	79.94 29	83.04 113	55.50 47	90.24 25	88.65 35	67.14 42	61.38 168	81.74 201	53.21 26	94.28 20	60.45 157	62.41 222	90.03 86
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
VDDNet		74.37 78	72.13 95	81.09 17	79.58 183	56.52 32	90.02 26	86.70 72	52.61 256	71.23 75	87.20 132	31.75 250	93.96 23	74.30 60	75.77 119	92.79 20

TSAR-MVS + GP.		77.82 31	77.59 30	78.49 60	85.25 68	50.27 186	90.02 26	90.57 9	56.58 219	74.26 41	91.60 46	54.26 21	92.16 52	75.87 46	79.91 88	93.05 15

DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 39	88.52 23	55.12 60	89.95 28	85.98 86	68.31 27	71.33 74	92.75 21	45.52 92	90.37 92	71.15 79	85.14 43	91.91 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		80.50 11	80.71 11	79.88 30	87.34 36	55.20 58	89.93 29	87.55 59	66.04 57	79.46 19	93.00 19	53.10 27	91.76 59	80.40 20	89.56 7	92.68 22

MG-MVS		78.42 21	76.99 37	82.73 2	93.17 1	64.46 1	89.93 29	88.51 43	64.83 71	73.52 49	88.09 120	48.07 60	92.19 51	62.24 138	84.53 49	91.53 48

VPNet		72.07 113	71.42 107	74.04 161	78.64 203	47.17 248	89.91 31	87.97 48	72.56 7	64.66 129	85.04 158	41.83 141	88.33 150	61.17 146	60.97 229	86.62 155

alignmvs		78.08 28	77.98 25	78.39 66	83.53 97	53.22 114	89.77 32	85.45 92	66.11 52	76.59 28	91.99 38	54.07 24	89.05 122	77.34 40	77.00 108	92.89 18

APDe-MVS		78.44 20	78.20 21	79.19 37	88.56 22	54.55 80	89.76 33	87.77 54	55.91 225	78.56 20	92.49 25	48.20 59	92.65 40	79.49 22	83.04 57	90.39 77

SteuartSystems-ACMMP		77.08 41	76.33 46	79.34 35	80.98 161	55.31 53	89.76 33	86.91 66	62.94 97	71.65 69	91.56 47	42.33 130	92.56 41	77.14 41	83.69 55	90.15 84
Skip Steuart: Steuart Systems R&D Blog.
Anonymous202405211		70.11 137	67.88 152	76.79 110	87.20 39	47.24 247	89.49 35	77.38 254	54.88 239	66.14 110	86.84 138	20.93 317	91.54 63	56.45 193	71.62 155	91.59 44

DP-MVS Recon		71.99 114	70.31 118	77.01 102	90.65 6	53.44 105	89.37 36	82.97 159	56.33 222	63.56 150	89.47 94	34.02 225	92.15 54	54.05 205	72.41 149	85.43 178

EPNet		78.36 23	78.49 19	77.97 78	85.49 59	52.04 142	89.36 37	84.07 135	73.22 4	77.03 25	91.72 43	49.32 55	90.17 101	73.46 68	82.77 58	91.69 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xxxxxxxxxxxxxcwj		77.31 38	76.54 41	79.61 31	85.35 64	56.34 36	89.31 38	72.84 300	61.55 118	74.63 36	92.38 27	47.75 64	91.35 68	78.18 34	86.85 24	91.15 58

save fliter					85.35 64	56.34 36	89.31 38	81.46 178	61.55 118

CSCG		80.41 12	79.72 12	82.49 5	89.12 21	57.67 15	89.29 40	91.54 3	59.19 162	71.82 68	90.05 84	59.72 6	96.04 7	78.37 29	88.40 12	93.75 5

MAR-MVS		76.76 48	75.60 53	80.21 23	90.87 5	54.68 76	89.14 41	89.11 22	62.95 96	70.54 80	92.33 29	41.05 146	94.95 15	57.90 180	86.55 28	91.00 64
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
CS-MVS		78.19 26	77.97 26	78.82 47	83.52 98	53.08 119	89.10 42	86.30 79	68.01 33	73.57 46	91.26 52	47.28 69	92.35 47	78.21 31	84.51 50	91.05 61

test_prior3		77.59 34	77.33 34	78.39 66	86.35 44	54.91 69	89.04 43	85.45 92	61.88 113	73.55 47	91.46 50	48.01 62	89.70 110	74.73 56	85.46 38	90.55 72

test_prior2							89.04 43		61.88 113	73.55 47	91.46 50	48.01 62		74.73 56	85.46 38

ET-MVSNet_ETH3D		75.23 69	74.08 72	78.67 56	84.52 78	55.59 45	88.92 45	89.21 20	68.06 32	53.13 265	90.22 78	49.71 52	87.62 173	72.12 75	70.82 162	92.82 19

PVSNet_Blended		76.53 51	76.54 41	76.50 112	85.91 48	51.83 149	88.89 46	84.24 132	67.82 35	69.09 85	89.33 99	46.70 76	88.13 157	75.43 50	81.48 71	89.55 95

Anonymous20240529		69.71 147	67.28 166	77.00 103	83.78 94	50.36 180	88.87 47	85.10 110	47.22 286	64.03 140	83.37 176	27.93 273	92.10 55	57.78 182	67.44 183	88.53 121

testtj		76.96 43	76.48 43	78.40 65	89.89 9	53.67 95	88.72 48	86.15 83	54.56 243	74.86 34	92.31 30	44.38 107	91.97 57	75.19 54	82.24 63	89.54 96

DPE-MVS		79.82 15	79.66 13	80.29 21	89.27 20	55.08 63	88.70 49	87.92 50	55.55 230	81.21 12	93.69 9	56.51 14	94.27 21	78.36 30	85.70 36	91.51 49
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
RRT_test8_iter05		72.74 100	71.20 110	77.36 91	87.25 38	53.51 99	88.68 50	89.53 16	65.20 68	61.32 169	81.27 206	45.89 86	92.48 44	65.99 109	55.65 280	86.10 163

PHI-MVS		77.49 35	77.00 36	78.95 41	85.33 66	50.69 169	88.57 51	88.59 41	58.14 185	73.60 45	93.31 12	43.14 125	93.79 25	73.81 63	88.53 11	92.37 27

WTY-MVS		77.47 36	77.52 31	77.30 93	88.33 26	46.25 260	88.46 52	90.32 10	71.40 10	72.32 64	91.72 43	53.44 25	92.37 46	66.28 108	75.42 122	93.28 10

ETH3D-3000-0.1		78.73 18	78.71 18	78.78 52	85.58 56	52.40 135	88.42 53	89.03 24	60.01 143	76.06 29	92.80 20	48.34 57	92.88 33	81.66 15	86.48 29	91.04 62

9.14			78.19 22		85.67 53		88.32 54	88.84 31	59.89 145	74.58 39	92.62 24	46.80 74	92.66 39	81.40 17	85.62 37

MVS_111021_HR		76.39 53	75.38 57	79.42 34	85.33 66	56.47 33	88.15 55	84.97 112	65.15 69	66.06 112	89.88 87	43.79 113	92.16 52	75.03 55	80.03 87	89.64 94

MS-PatchMatch		72.34 108	71.26 108	75.61 130	82.38 133	55.55 46	88.00 56	89.95 15	65.38 63	56.51 241	80.74 212	32.28 243	92.89 32	57.95 179	88.10 13	78.39 277

HQP-NCC					79.02 192		88.00 56		65.45 59	64.48 133

ACMP_Plane					79.02 192		88.00 56		65.45 59	64.48 133

HQP-MVS		72.34 108	71.44 106	75.03 141	79.02 192	51.56 154	88.00 56	83.68 142	65.45 59	64.48 133	85.13 156	37.35 187	88.62 137	66.70 103	73.12 141	84.91 185

canonicalmvs		78.17 27	77.86 28	79.12 40	84.30 81	54.22 84	87.71 60	84.57 123	67.70 38	77.70 22	92.11 35	50.90 42	89.95 104	78.18 34	77.54 103	93.20 12

VPA-MVSNet		71.12 122	70.66 114	72.49 195	78.75 198	44.43 279	87.64 61	90.02 13	63.97 80	65.02 125	81.58 203	42.14 134	87.42 178	63.42 131	63.38 211	85.63 175

test_8					85.72 50	55.31 53	87.60 62	83.88 139	57.84 193	72.84 56	90.99 55	44.99 97	88.34 149

Regformer-1		77.80 32	77.44 32	78.88 44	87.78 32	52.44 134	87.60 62	90.08 12	68.86 24	72.49 62	91.79 40	47.69 66	94.90 16	73.57 66	77.05 105	89.31 99

Regformer-2		77.15 40	76.82 39	78.14 72	87.78 32	51.84 148	87.60 62	89.12 21	67.23 41	71.93 67	91.79 40	46.03 84	93.53 27	72.85 72	77.05 105	89.05 108

TEST9					85.68 51	55.42 49	87.59 65	84.00 136	57.72 195	72.99 53	90.98 56	44.87 100	88.58 139

train_agg		76.91 44	76.40 45	78.45 63	85.68 51	55.42 49	87.59 65	84.00 136	57.84 193	72.99 53	90.98 56	44.99 97	88.58 139	78.19 32	85.32 41	91.34 56

SMA-MVS		79.10 17	78.76 17	80.12 26	84.42 79	55.87 43	87.58 67	86.76 70	61.48 122	80.26 16	93.10 15	46.53 78	92.41 45	79.97 21	88.77 9	92.08 33
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
agg_prior1		76.68 50	76.24 48	78.00 76	85.64 54	54.92 67	87.55 68	83.61 145	57.99 189	72.53 60	91.05 53	45.36 93	88.10 159	77.76 38	84.68 48	90.99 65

plane_prior						49.57 197	87.43 69		64.57 73						72.84 145

TR-MVS		69.71 147	67.85 154	75.27 138	82.94 118	48.48 225	87.40 70	80.86 188	57.15 207	64.61 131	87.08 135	32.67 239	89.64 113	46.38 253	71.55 157	87.68 136

CDPH-MVS		76.05 58	75.19 58	78.62 58	86.51 43	54.98 66	87.32 71	84.59 122	58.62 179	70.75 78	90.85 62	43.10 127	90.63 87	70.50 82	84.51 50	90.24 80

3Dnovator+	62.71 7	72.29 110	70.50 115	77.65 85	83.40 102	51.29 163	87.32 71	86.40 77	59.01 171	58.49 209	88.32 115	32.40 241	91.27 70	57.04 188	82.15 66	90.38 78

API-MVS		74.17 81	72.07 98	80.49 19	90.02 8	58.55 9	87.30 73	84.27 129	57.51 201	65.77 117	87.77 127	41.61 143	95.97 8	51.71 222	82.63 59	86.94 146

BH-RMVSNet		70.08 139	68.01 149	76.27 116	84.21 85	51.22 165	87.29 74	79.33 219	58.96 174	63.63 149	86.77 139	33.29 234	90.30 97	44.63 261	73.96 133	87.30 143

MTMP							87.27 75	15.34 363

APD-MVS		76.15 56	75.68 51	77.54 87	88.52 23	53.44 105	87.26 76	85.03 111	53.79 247	74.91 33	91.68 45	43.80 112	90.31 95	74.36 59	81.82 67	88.87 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EIA-MVS		75.92 60	75.18 59	78.13 73	85.14 69	51.60 153	87.17 77	85.32 99	64.69 72	68.56 88	90.53 70	45.79 89	91.58 62	67.21 100	82.18 65	91.20 57

test_prior4						56.39 35	87.15 78

casdiffmvs		77.36 37	76.85 38	78.88 44	80.40 176	54.66 78	87.06 79	85.88 87	72.11 8	71.57 71	88.63 113	50.89 44	90.35 93	76.00 45	79.11 93	91.63 43

cascas		69.01 159	66.13 187	77.66 84	79.36 184	55.41 51	86.99 80	83.75 141	56.69 216	58.92 199	81.35 205	24.31 298	92.10 55	53.23 208	70.61 163	85.46 177

nrg030		72.27 112	71.56 103	74.42 152	75.93 243	50.60 171	86.97 81	83.21 153	62.75 99	67.15 100	84.38 162	50.07 48	86.66 196	71.19 78	62.37 223	85.99 164

#test#		74.86 76	73.78 76	78.10 74	84.30 81	53.68 93	86.95 82	84.36 126	59.00 172	65.78 115	90.56 67	40.70 151	90.90 79	71.48 77	80.88 73	89.71 91

114514_t		69.87 145	67.88 152	75.85 128	88.38 25	52.35 138	86.94 83	83.68 142	53.70 248	55.68 247	85.60 152	30.07 262	91.20 71	55.84 195	71.02 160	83.99 197

CP-MVS		72.59 105	71.46 105	76.00 126	82.93 119	52.32 139	86.93 84	82.48 164	55.15 234	63.65 148	90.44 75	35.03 218	88.53 143	68.69 92	77.83 101	87.15 144

ZNCC-MVS		75.82 64	75.02 61	78.23 70	83.88 93	53.80 91	86.91 85	86.05 85	59.71 147	67.85 95	90.55 69	42.23 132	91.02 75	72.66 74	85.29 42	89.87 90

PAPM		76.76 48	76.07 50	78.81 48	80.20 177	59.11 7	86.86 86	86.23 81	68.60 25	70.18 82	88.84 107	51.57 36	87.16 182	65.48 115	86.68 26	90.15 84

Fast-Effi-MVS+		72.73 101	71.15 112	77.48 88	82.75 125	54.76 71	86.77 87	80.64 191	63.05 95	65.93 113	84.01 165	44.42 106	89.03 123	56.45 193	76.36 115	88.64 117

ETH3D cwj APD-0.16		78.36 23	78.19 22	78.86 46	84.21 85	52.68 128	86.70 88	89.02 25	59.13 168	75.37 31	92.49 25	49.06 56	93.20 29	80.67 19	87.08 21	90.71 70

thisisatest0515		73.64 91	72.20 93	77.97 78	81.63 144	53.01 122	86.69 89	88.81 32	62.53 102	64.06 139	85.65 151	52.15 33	92.50 42	58.43 169	69.84 168	88.39 123

SF-MVS		77.64 33	77.42 33	78.32 69	83.75 95	52.47 133	86.63 90	87.80 51	58.78 176	74.63 36	92.38 27	47.75 64	91.35 68	78.18 34	86.85 24	91.15 58

BH-w/o		70.02 141	68.51 142	74.56 148	82.77 123	50.39 178	86.60 91	78.14 240	59.77 146	59.65 183	85.57 153	39.27 165	87.30 180	49.86 231	74.94 129	85.99 164

mvs-test1		69.04 157	67.57 160	73.44 179	75.17 249	51.68 152	86.57 92	74.48 284	62.15 106	62.07 164	85.79 149	30.59 258	87.48 176	65.40 120	65.94 194	81.18 248

DeepC-MVS	67.15 4	76.90 46	76.27 47	78.80 49	80.70 169	55.02 64	86.39 93	86.71 71	66.96 44	67.91 94	89.97 86	48.03 61	91.41 67	75.60 49	84.14 52	89.96 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TransMVSNet (Re)		62.82 238	60.76 240	69.02 252	73.98 265	41.61 303	86.36 94	79.30 220	56.90 209	52.53 268	76.44 255	41.85 140	87.60 174	38.83 278	40.61 331	77.86 283

MSLP-MVS++		74.21 80	72.25 92	80.11 27	81.45 155	56.47 33	86.32 95	79.65 209	58.19 184	66.36 108	92.29 31	36.11 207	90.66 85	67.39 98	82.49 60	93.18 13

QAPM		71.88 115	69.33 134	79.52 32	82.20 135	54.30 83	86.30 96	88.77 33	56.61 218	59.72 182	87.48 130	33.90 228	95.36 11	47.48 246	81.49 70	88.90 111

WR-MVS		67.58 186	66.76 174	70.04 244	75.92 244	45.06 275	86.23 97	85.28 102	64.31 75	58.50 208	81.00 207	44.80 104	82.00 263	49.21 236	55.57 281	83.06 218

PVSNet_BlendedMVS		73.42 93	73.30 78	73.76 170	85.91 48	51.83 149	86.18 98	84.24 132	65.40 62	69.09 85	80.86 210	46.70 76	88.13 157	75.43 50	65.92 195	81.33 244

ETV-MVS		77.17 39	76.74 40	78.48 61	81.80 140	54.55 80	86.13 99	85.33 98	68.20 29	73.10 52	90.52 71	45.23 95	90.66 85	79.37 23	80.95 72	90.22 81

AdaColmap		67.86 181	65.48 201	75.00 142	88.15 29	54.99 65	86.10 100	76.63 268	49.30 276	57.80 218	86.65 142	29.39 265	88.94 132	45.10 259	70.21 166	81.06 249

OPM-MVS		70.75 130	69.58 129	74.26 157	75.55 248	51.34 161	86.05 101	83.29 152	61.94 112	62.95 155	85.77 150	34.15 224	88.44 145	65.44 119	71.07 159	82.99 219

Vis-MVSNet		70.61 132	69.34 133	74.42 152	80.95 165	48.49 224	86.03 102	77.51 251	58.74 177	65.55 119	87.78 126	34.37 222	85.95 220	52.53 220	80.61 77	88.80 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Anonymous20231211		66.08 217	63.67 221	73.31 181	83.07 112	48.75 217	86.01 103	84.67 121	45.27 302	56.54 239	76.67 253	28.06 272	88.95 130	52.78 215	59.95 232	82.23 226

EG-PatchMatch MVS		62.40 245	59.59 247	70.81 231	73.29 270	49.05 208	85.81 104	84.78 118	51.85 263	44.19 306	73.48 282	15.52 338	89.85 105	40.16 275	67.24 184	73.54 318

PVSNet_Blended_VisFu		73.40 94	72.44 88	76.30 115	81.32 159	54.70 75	85.81 104	78.82 226	63.70 85	64.53 132	85.38 155	47.11 72	87.38 179	67.75 97	77.55 102	86.81 153

HQP_MVS		70.96 126	69.91 126	74.12 159	77.95 214	49.57 197	85.76 106	82.59 162	63.60 88	62.15 162	83.28 178	36.04 210	88.30 152	65.46 116	72.34 150	84.49 188

plane_prior2							85.76 106		63.60 88

GST-MVS		74.87 75	73.90 74	77.77 81	83.30 104	53.45 104	85.75 108	85.29 101	59.22 161	66.50 107	89.85 88	40.94 147	90.76 82	70.94 80	83.35 56	89.10 107

SD-MVS		76.18 55	74.85 64	80.18 24	85.39 63	56.90 25	85.75 108	82.45 165	56.79 214	74.48 40	91.81 39	43.72 116	90.75 83	74.61 58	78.65 96	92.91 17
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CHOSEN 1792x2688		76.24 54	74.03 73	82.88 1	83.09 110	62.84 2	85.73 110	85.39 95	69.79 19	64.87 128	83.49 174	41.52 144	93.69 26	70.55 81	81.82 67	92.12 32

FMVSNet3		68.84 161	67.40 164	73.19 183	85.05 70	48.53 222	85.71 111	85.36 96	60.90 132	57.58 224	79.15 223	42.16 133	86.77 192	47.25 248	63.40 208	84.27 192

MP-MVS		74.99 74	74.33 70	76.95 105	82.89 120	53.05 121	85.63 112	83.50 147	57.86 192	67.25 99	90.24 77	43.38 122	88.85 134	76.03 44	82.23 64	88.96 110
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HFP-MVS		74.37 78	73.13 81	78.10 74	84.30 81	53.68 93	85.58 113	84.36 126	56.82 212	65.78 115	90.56 67	40.70 151	90.90 79	69.18 89	80.88 73	89.71 91

ACMMPR		73.76 88	72.61 84	77.24 98	83.92 91	52.96 124	85.58 113	84.29 128	56.82 212	65.12 122	90.45 72	37.24 191	90.18 100	69.18 89	80.84 75	88.58 119

BH-untuned		68.28 174	66.40 179	73.91 164	81.62 145	50.01 191	85.56 115	77.39 253	57.63 198	57.47 229	83.69 172	36.36 205	87.08 184	44.81 260	73.08 144	84.65 187

region2R		73.75 89	72.55 86	77.33 92	83.90 92	52.98 123	85.54 116	84.09 134	56.83 211	65.10 123	90.45 72	37.34 189	90.24 98	68.89 91	80.83 76	88.77 115

xiu_mvs_v1_base_debu		71.60 118	70.29 119	75.55 131	77.26 225	53.15 115	85.34 117	79.37 213	55.83 226	72.54 57	90.19 79	22.38 308	86.66 196	73.28 69	76.39 112	86.85 150

xiu_mvs_v1_base		71.60 118	70.29 119	75.55 131	77.26 225	53.15 115	85.34 117	79.37 213	55.83 226	72.54 57	90.19 79	22.38 308	86.66 196	73.28 69	76.39 112	86.85 150

xiu_mvs_v1_base_debi		71.60 118	70.29 119	75.55 131	77.26 225	53.15 115	85.34 117	79.37 213	55.83 226	72.54 57	90.19 79	22.38 308	86.66 196	73.28 69	76.39 112	86.85 150

NR-MVSNet		67.25 196	65.99 190	71.04 228	73.27 271	43.91 283	85.32 120	84.75 119	66.05 56	53.65 263	82.11 197	45.05 96	85.97 219	47.55 245	56.18 272	83.24 213

Effi-MVS+		75.24 68	73.61 77	80.16 25	81.92 138	57.42 20	85.21 121	76.71 266	60.68 136	73.32 51	89.34 97	47.30 68	91.63 61	68.28 94	79.72 90	91.42 51

无先验							85.19 122	78.00 243	49.08 277				85.13 235	52.78 215		87.45 140

FMVSNet2		67.57 187	65.79 194	72.90 186	82.71 126	47.97 239	85.15 123	84.93 113	58.55 180	56.71 237	78.26 230	36.72 201	86.67 195	46.15 255	62.94 219	84.07 194

test-LLR		69.65 150	69.01 138	71.60 217	78.67 200	48.17 232	85.13 124	79.72 206	59.18 164	63.13 153	82.58 189	36.91 196	80.24 277	60.56 153	75.17 124	86.39 160

TESTMET0.1,1		72.86 99	72.33 89	74.46 150	81.98 137	50.77 167	85.13 124	85.47 91	66.09 53	67.30 97	83.69 172	37.27 190	83.57 252	65.06 123	78.97 95	89.05 108

test-mter		68.36 171	67.29 165	71.60 217	78.67 200	48.17 232	85.13 124	79.72 206	53.38 250	63.13 153	82.58 189	27.23 279	80.24 277	60.56 153	75.17 124	86.39 160

Regformer-3		76.02 59	75.47 55	77.70 83	85.49 59	51.47 157	85.12 127	90.19 11	68.52 26	69.36 83	90.66 65	46.45 79	94.81 17	70.25 84	73.16 139	86.81 153

Regformer-4		75.06 73	74.59 68	76.47 113	85.49 59	50.33 182	85.12 127	88.61 39	66.42 46	68.48 89	90.66 65	44.15 108	92.68 38	69.24 88	73.16 139	86.39 160

1112_ss		70.05 140	69.37 132	72.10 202	80.77 168	42.78 293	85.12 127	76.75 265	59.69 148	61.19 171	92.12 33	47.48 67	83.84 247	53.04 211	68.21 177	89.66 93

XXY-MVS		70.18 136	69.28 136	72.89 188	77.64 218	42.88 292	85.06 130	87.50 60	62.58 101	62.66 159	82.34 194	43.64 118	89.83 106	58.42 171	63.70 207	85.96 166

MSP-MVS		82.30 5	83.47 1	78.80 49	82.99 116	52.71 127	85.04 131	88.63 37	66.08 54	86.77 2	92.75 21	72.05 1	91.46 66	83.35 6	93.53 1	92.23 29
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
thres200		68.71 167	67.27 167	73.02 184	84.73 75	46.76 251	85.03 132	87.73 55	62.34 105	59.87 179	83.45 175	43.15 124	88.32 151	31.25 314	67.91 181	83.98 199

MVP-Stereo		70.97 125	70.44 116	72.59 192	76.03 242	51.36 160	85.02 133	86.99 65	60.31 140	56.53 240	78.92 225	40.11 158	90.00 102	60.00 162	90.01 4	76.41 299
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PS-MVSNAJss		68.78 166	67.17 168	73.62 176	73.01 273	48.33 230	84.95 134	84.81 117	59.30 160	58.91 200	79.84 216	37.77 176	88.86 133	62.83 134	63.12 217	83.67 206

v2v482		69.55 153	67.64 157	75.26 139	72.32 283	53.83 90	84.93 135	81.94 169	65.37 64	60.80 173	79.25 221	41.62 142	88.98 129	63.03 133	59.51 236	82.98 220

XVS		72.92 98	71.62 102	76.81 107	83.41 99	52.48 131	84.88 136	83.20 154	58.03 186	63.91 143	89.63 92	35.50 214	89.78 107	65.50 113	80.50 79	88.16 124

X-MVStestdata		65.85 219	62.20 228	76.81 107	83.41 99	52.48 131	84.88 136	83.20 154	58.03 186	63.91 143	4.82 361	35.50 214	89.78 107	65.50 113	80.50 79	88.16 124

Fast-Effi-MVS+-dtu		66.53 210	64.10 219	73.84 167	72.41 281	52.30 140	84.73 138	75.66 275	59.51 151	56.34 242	79.11 224	28.11 271	85.85 222	57.74 183	63.29 212	83.35 209

hse-mvs3		73.95 84	72.89 83	77.15 99	80.17 178	50.37 179	84.68 139	83.33 148	68.08 30	71.97 66	88.65 112	42.50 129	91.15 73	78.82 26	57.78 260	89.91 89

v1144		68.81 163	66.82 171	74.80 146	72.34 282	53.46 101	84.68 139	81.77 175	64.25 76	60.28 178	77.91 232	40.23 155	88.95 130	60.37 158	59.52 235	81.97 228

CANet_DTU		73.71 90	73.14 79	75.40 134	82.61 130	50.05 190	84.67 141	79.36 216	69.72 20	75.39 30	90.03 85	29.41 264	85.93 221	67.99 96	79.11 93	90.22 81

PVSNet	62.49 8	69.27 155	67.81 155	73.64 174	84.41 80	51.85 147	84.63 142	77.80 245	66.42 46	59.80 181	84.95 159	22.14 312	80.44 275	55.03 198	75.11 126	88.62 118

mPP-MVS		71.79 117	70.38 117	76.04 124	82.65 129	52.06 141	84.45 143	81.78 174	55.59 229	62.05 165	89.68 91	33.48 232	88.28 154	65.45 118	78.24 100	87.77 134

CL-MVSNet_2432*1600		62.98 236	61.14 237	68.50 263	65.86 322	42.96 290	84.37 144	82.98 158	60.98 130	53.95 259	72.70 289	40.43 153	83.71 250	41.10 273	47.93 309	78.83 269

OpenMVS	61.00 11	69.99 143	67.55 161	77.30 93	78.37 210	54.07 89	84.36 145	85.76 89	57.22 206	56.71 237	87.67 128	30.79 257	92.83 34	43.04 267	84.06 54	85.01 183

MP-MVS-pluss		75.54 66	75.03 60	77.04 100	81.37 157	52.65 130	84.34 146	84.46 124	61.16 125	69.14 84	91.76 42	39.98 160	88.99 128	78.19 32	84.89 46	89.48 97
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPR		75.20 70	74.13 71	78.41 64	88.31 27	55.10 62	84.31 147	85.66 90	63.76 84	67.55 96	90.73 64	43.48 121	89.40 116	66.36 107	77.03 107	90.73 69

SR-MVS		70.92 127	69.73 128	74.50 149	83.38 103	50.48 175	84.27 148	79.35 217	48.96 279	66.57 106	90.45 72	33.65 231	87.11 183	66.42 105	74.56 130	85.91 167

v148		68.24 176	66.35 180	73.88 165	71.76 286	51.47 157	84.23 149	81.90 173	63.69 86	58.94 197	76.44 255	43.72 116	87.78 169	60.63 151	55.86 277	82.39 225

UniMVSNet_NR-MVSNet		68.82 162	68.29 146	70.40 237	75.71 246	42.59 295	84.23 149	86.78 69	66.31 48	58.51 206	82.45 191	51.57 36	84.64 243	53.11 209	55.96 275	83.96 201

v144192		67.86 181	65.76 195	74.16 158	71.68 287	53.09 118	84.14 151	80.83 189	62.85 98	59.21 193	77.28 242	39.30 164	88.00 162	58.67 168	57.88 258	81.40 241

UGNet		68.71 167	67.11 169	73.50 178	80.55 174	47.61 241	84.08 152	78.51 234	59.45 152	65.68 118	82.73 187	23.78 300	85.08 237	52.80 214	76.40 111	87.80 133
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
PMMVS		72.98 97	72.05 99	75.78 129	83.57 96	48.60 219	84.08 152	82.85 161	61.62 117	68.24 92	90.33 76	28.35 268	87.78 169	72.71 73	76.69 110	90.95 66

ACMMP_NAP		76.43 52	75.66 52	78.73 53	81.92 138	54.67 77	84.06 154	85.35 97	61.10 127	72.99 53	91.50 48	40.25 154	91.00 76	76.84 42	86.98 22	90.51 76

v1192		67.96 180	65.74 196	74.63 147	71.79 285	53.43 107	84.06 154	80.99 187	63.19 94	59.56 186	77.46 239	37.50 186	88.65 136	58.20 174	58.93 242	81.79 231

zzz-MVS		74.15 82	73.11 82	77.27 95	81.54 150	53.57 97	84.02 156	81.31 181	59.41 154	68.39 90	90.96 58	36.07 208	89.01 124	73.80 64	82.45 61	89.23 101

FIs		70.00 142	70.24 122	69.30 250	77.93 216	38.55 316	83.99 157	87.72 56	66.86 45	57.66 222	84.17 164	52.28 31	85.31 228	52.72 219	68.80 174	84.02 195

MVS_Test		75.85 61	74.93 63	78.62 58	84.08 87	55.20 58	83.99 157	85.17 106	68.07 31	73.38 50	82.76 184	50.44 46	89.00 126	65.90 111	80.61 77	91.64 42

baseline		76.86 47	76.24 48	78.71 54	80.47 175	54.20 87	83.90 159	84.88 115	71.38 11	71.51 72	89.15 102	50.51 45	90.55 89	75.71 47	78.65 96	91.39 52

baseline2		75.15 71	74.54 69	76.98 104	81.67 143	51.74 151	83.84 160	91.94 1	69.97 18	58.98 196	86.02 147	59.73 5	91.73 60	68.37 93	70.40 165	87.48 138

EPP-MVSNet		71.14 121	70.07 124	74.33 155	79.18 189	46.52 254	83.81 161	86.49 74	56.32 223	57.95 215	84.90 160	54.23 22	89.14 119	58.14 175	69.65 170	87.33 141

原ACMM2							83.77 162

v1921920		67.45 190	65.23 208	74.10 160	71.51 290	52.90 125	83.75 163	80.44 194	62.48 104	59.12 195	77.13 243	36.98 194	87.90 163	57.53 184	58.14 252	81.49 236

OpenMVS_ROB	53.19 17	59.20 260	56.00 271	68.83 255	71.13 295	44.30 280	83.64 164	75.02 281	46.42 294	46.48 302	73.03 285	18.69 325	88.14 156	27.74 327	61.80 225	74.05 314

MVSTER		73.25 95	72.33 89	76.01 125	85.54 58	53.76 92	83.52 165	87.16 62	67.06 43	63.88 145	81.66 202	52.77 28	90.44 90	64.66 125	64.69 200	83.84 204

GBi-Net		67.09 200	65.47 202	71.96 208	82.71 126	46.36 256	83.52 165	83.31 149	58.55 180	57.58 224	76.23 259	36.72 201	86.20 205	47.25 248	63.40 208	83.32 210

test1		67.09 200	65.47 202	71.96 208	82.71 126	46.36 256	83.52 165	83.31 149	58.55 180	57.58 224	76.23 259	36.72 201	86.20 205	47.25 248	63.40 208	83.32 210

FMVSNet1		64.57 224	62.11 229	71.96 208	77.32 223	46.36 256	83.52 165	83.31 149	52.43 258	54.42 254	76.23 259	27.80 275	86.20 205	42.59 271	61.34 228	83.32 210

baseline1		72.51 106	72.12 97	73.69 172	85.05 70	44.46 277	83.51 169	86.13 84	71.61 9	64.64 130	87.97 123	55.00 20	89.48 115	59.07 164	56.05 274	87.13 145

CDS-MVSNet		70.48 134	69.43 130	73.64 174	77.56 220	48.83 216	83.51 169	77.45 252	63.27 92	62.33 161	85.54 154	43.85 110	83.29 256	57.38 187	74.00 132	88.79 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 135	68.56 141	76.20 119	79.78 181	51.52 156	83.49 171	88.58 42	57.62 199	58.60 205	82.79 183	51.03 41	91.48 65	52.84 213	62.36 224	85.59 176

Test_1112_low_res		67.18 198	66.23 184	70.02 245	78.75 198	41.02 307	83.43 172	73.69 292	57.29 205	58.45 211	82.39 193	45.30 94	80.88 270	50.50 227	66.26 193	88.16 124

ACMMP		70.81 129	69.29 135	75.39 135	81.52 154	51.92 146	83.43 172	83.03 157	56.67 217	58.80 203	88.91 105	31.92 248	88.58 139	65.89 112	73.39 138	85.67 172
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
tfpn200view9		67.57 187	66.13 187	71.89 215	84.05 88	45.07 272	83.40 174	87.71 57	60.79 133	57.79 219	82.76 184	43.53 119	87.80 166	28.80 320	66.36 190	82.78 223

thres400		67.40 194	66.13 187	71.19 225	84.05 88	45.07 272	83.40 174	87.71 57	60.79 133	57.79 219	82.76 184	43.53 119	87.80 166	28.80 320	66.36 190	80.71 254

v1240		66.99 203	64.68 213	73.93 163	71.38 293	52.66 129	83.39 176	79.98 200	61.97 111	58.44 212	77.11 244	35.25 216	87.81 165	56.46 192	58.15 250	81.33 244

Baseline_NR-MVSNet		65.49 222	64.27 217	69.13 251	74.37 262	41.65 302	83.39 176	78.85 224	59.56 150	59.62 185	76.88 250	40.75 148	87.44 177	49.99 229	55.05 282	78.28 279

miper_enhance_ethall		69.77 146	68.90 139	72.38 198	78.93 195	49.91 193	83.29 178	78.85 224	64.90 70	59.37 189	79.46 218	52.77 28	85.16 234	63.78 128	58.72 243	82.08 227

diffmvs		75.11 72	74.65 67	76.46 114	78.52 206	53.35 109	83.28 179	79.94 201	70.51 15	71.64 70	88.72 108	46.02 85	86.08 215	77.52 39	75.75 120	89.96 87

ACMP	61.11 9	66.24 215	64.33 216	72.00 207	74.89 256	49.12 206	83.18 180	79.83 204	55.41 232	52.29 270	82.68 188	25.83 287	86.10 211	60.89 148	63.94 205	80.78 252
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GA-MVS		69.04 157	66.70 176	76.06 123	75.11 251	52.36 137	83.12 181	80.23 197	63.32 91	60.65 175	79.22 222	30.98 256	88.37 147	61.25 145	66.41 189	87.46 139

3Dnovator	64.70 6	74.46 77	72.48 87	80.41 20	82.84 122	55.40 52	83.08 182	88.61 39	67.61 39	59.85 180	88.66 109	34.57 221	93.97 22	58.42 171	88.70 10	91.85 40

PGM-MVS		72.60 103	71.20 110	76.80 109	82.95 117	52.82 126	83.07 183	82.14 166	56.51 220	63.18 152	89.81 89	35.68 213	89.76 109	67.30 99	80.19 83	87.83 132

LPG-MVS_test		66.44 212	64.58 214	72.02 205	74.42 260	48.60 219	83.07 183	80.64 191	54.69 241	53.75 261	83.83 168	25.73 289	86.98 186	60.33 159	64.71 198	80.48 256

TranMVSNet+NR-MVSNet		66.94 205	65.61 199	70.93 230	73.45 268	43.38 288	83.02 185	84.25 130	65.31 66	58.33 213	81.90 200	39.92 161	85.52 224	49.43 234	54.89 284	83.89 203

test0.0.03 1		62.54 240	62.44 226	62.86 300	72.28 284	29.51 342	82.93 186	78.78 227	59.18 164	53.07 266	82.41 192	36.91 196	77.39 304	37.45 281	58.96 241	81.66 234

pm-mvs1		64.12 227	62.56 225	68.78 257	71.68 287	38.87 315	82.89 187	81.57 176	55.54 231	53.89 260	77.82 234	37.73 179	86.74 193	48.46 241	53.49 294	80.72 253

DWT-MVSNet_test		75.47 67	73.87 75	80.29 21	87.33 37	57.05 23	82.86 188	87.96 49	72.59 6	67.29 98	87.79 125	51.61 35	91.52 64	54.75 202	72.63 147	92.29 28

EPNet_dtu		66.25 214	66.71 175	64.87 290	78.66 202	34.12 328	82.80 189	75.51 276	61.75 115	64.47 136	86.90 137	37.06 193	72.46 328	43.65 265	69.63 171	88.02 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1172		69.64 151	68.38 145	73.41 180	82.77 123	48.84 215	82.79 190	78.34 238	47.02 289	65.27 121	90.07 83	31.17 254	86.09 213	64.51 126	73.49 137	85.31 179

pmmvs5		62.80 239	61.18 236	67.66 267	69.53 304	42.37 300	82.65 191	75.19 280	54.30 246	52.03 273	78.51 229	31.64 251	80.67 271	48.60 239	58.15 250	79.95 263

cl-mvsnet_		67.43 191	65.93 191	71.95 211	76.33 235	48.02 237	82.58 192	79.12 221	61.30 124	56.72 236	76.92 248	46.12 81	86.44 203	57.98 177	56.31 269	81.38 243

cl-mvsnet1		67.43 191	65.93 191	71.94 212	76.33 235	48.01 238	82.57 193	79.11 222	61.31 123	56.73 235	76.92 248	46.09 82	86.43 204	57.98 177	56.31 269	81.39 242

TAMVS		69.51 154	68.16 148	73.56 177	76.30 237	48.71 218	82.57 193	77.17 257	62.10 108	61.32 169	84.23 163	41.90 139	83.46 254	54.80 201	73.09 143	88.50 122

EI-MVSNet-Vis-set		73.19 96	72.60 85	74.99 143	82.56 131	49.80 195	82.55 195	89.00 26	66.17 51	65.89 114	88.98 103	43.83 111	92.29 48	65.38 122	69.01 173	82.87 222

DP-MVS		59.24 259	56.12 270	68.63 260	88.24 28	50.35 181	82.51 196	64.43 329	41.10 322	46.70 300	78.77 226	24.75 297	88.57 142	22.26 339	56.29 271	66.96 337

miper_ehance_all_eth		68.70 169	67.58 158	72.08 203	76.91 231	49.48 202	82.47 197	78.45 236	62.68 100	58.28 214	77.88 233	50.90 42	85.01 238	61.91 141	58.72 243	81.75 232

cl-mvsnet2		68.85 160	67.69 156	72.35 199	78.07 213	49.98 192	82.45 198	78.48 235	62.50 103	58.46 210	77.95 231	49.99 49	85.17 233	62.55 135	58.72 243	81.90 229

UniMVSNet (Re)		67.71 184	66.80 172	70.45 235	74.44 259	42.93 291	82.42 199	84.90 114	63.69 86	59.63 184	80.99 208	47.18 70	85.23 232	51.17 225	56.75 266	83.19 215

v8		67.25 196	64.99 211	74.04 161	72.89 276	53.31 112	82.37 200	80.11 199	61.54 120	54.29 256	76.02 264	42.89 128	88.41 146	58.43 169	56.36 267	80.39 258

RRT_MVS		65.43 223	64.01 220	69.68 247	81.54 150	50.15 187	82.31 201	76.78 264	55.25 233	60.64 176	82.00 199	25.18 293	79.00 287	60.96 147	51.45 301	79.89 264

ACMM	58.35 12	64.35 226	62.01 230	71.38 221	74.21 263	48.51 223	82.25 202	79.66 208	47.61 283	54.54 253	80.11 214	25.26 292	86.00 216	51.26 223	63.16 215	79.64 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres600view7		66.46 211	65.12 209	70.47 234	83.41 99	43.80 285	82.15 203	87.78 52	59.37 156	56.02 244	82.21 195	43.73 114	86.90 190	26.51 331	64.94 197	80.71 254

cl_fuxian		67.97 179	66.66 177	71.91 214	76.20 239	49.31 204	82.13 204	78.00 243	61.99 110	57.64 223	76.94 247	49.41 53	84.93 239	60.62 152	57.01 264	81.49 236

Effi-MVS+-dtu		66.24 215	64.96 212	70.08 242	75.17 249	49.64 196	82.01 205	74.48 284	62.15 106	57.83 217	76.08 263	30.59 258	83.79 248	65.40 120	60.93 230	76.81 292

our_test_3		59.11 262	55.08 278	71.18 226	71.42 291	53.29 113	81.96 206	74.52 283	48.32 280	42.08 315	69.28 312	28.14 270	82.15 260	34.35 301	45.68 321	78.11 282

CPTT-MVS		67.15 199	65.84 193	71.07 227	80.96 162	50.32 183	81.94 207	74.10 287	46.18 297	57.91 216	87.64 129	29.57 263	81.31 266	64.10 127	70.18 167	81.56 235

APD-MVS_3200maxsize		69.62 152	68.23 147	73.80 169	81.58 148	48.22 231	81.91 208	79.50 212	48.21 281	64.24 138	89.75 90	31.91 249	87.55 175	63.08 132	73.85 135	85.64 174

v10		66.61 209	64.20 218	73.83 168	72.59 279	53.37 108	81.88 209	79.91 203	61.11 126	54.09 258	75.60 266	40.06 159	88.26 155	56.47 191	56.10 273	79.86 265

EI-MVSNet-UG-set		72.37 107	71.73 101	74.29 156	81.60 146	49.29 205	81.85 210	88.64 36	65.29 67	65.05 124	88.29 116	43.18 123	91.83 58	63.74 129	67.97 180	81.75 232

ppachtmachnet_test		58.56 270	54.34 279	71.24 223	71.42 291	54.74 72	81.84 211	72.27 303	49.02 278	45.86 305	68.99 313	26.27 284	83.30 255	30.12 316	43.23 326	75.69 302

test_0402		56.45 282	53.03 286	66.69 277	76.78 232	50.31 184	81.76 212	69.61 319	42.79 318	43.88 307	72.13 296	22.82 306	86.46 202	16.57 349	50.94 302	63.31 343

旧先验2							81.73 213		45.53 300	74.66 35			70.48 335	58.31 173

thres100view900		66.87 206	65.42 205	71.24 223	83.29 105	43.15 289	81.67 214	87.78 52	59.04 170	55.92 245	82.18 196	43.73 114	87.80 166	28.80 320	66.36 190	82.78 223

MVSFormer		73.53 92	72.19 94	77.57 86	83.02 114	55.24 55	81.63 215	81.44 179	50.28 270	76.67 26	90.91 60	44.82 102	86.11 209	60.83 149	80.09 84	91.36 54

test_djsdf		63.84 229	61.56 233	70.70 232	68.78 308	44.69 276	81.63 215	81.44 179	50.28 270	52.27 271	76.26 258	26.72 282	86.11 209	60.83 149	55.84 278	81.29 247

新几何2							81.61 217

TSAR-MVS + MP.		78.31 25	78.26 20	78.48 61	81.33 158	56.31 38	81.59 218	86.41 76	69.61 21	81.72 11	88.16 119	55.09 19	88.04 161	74.12 61	86.31 30	91.09 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS-dyc-post		68.27 175	66.87 170	72.48 196	80.96 162	48.14 234	81.54 219	76.98 260	46.42 294	62.75 157	89.42 95	31.17 254	86.09 213	60.52 155	72.06 153	83.19 215

RE-MVS-def			66.66 177		80.96 162	48.14 234	81.54 219	76.98 260	46.42 294	62.75 157	89.42 95	29.28 266		60.52 155	72.06 153	83.19 215

V42		67.66 185	65.60 200	73.86 166	70.69 298	53.63 96	81.50 221	78.61 232	63.85 82	59.49 188	77.49 238	37.98 173	87.65 172	62.33 136	58.43 247	80.29 259

DU-MVS		66.84 207	65.74 196	70.16 240	73.27 271	42.59 295	81.50 221	82.92 160	63.53 90	58.51 206	82.11 197	40.75 148	84.64 243	53.11 209	55.96 275	83.24 213

HyFIR lowres test		69.94 144	67.58 158	77.04 100	77.11 230	57.29 21	81.49 223	79.11 222	58.27 183	58.86 201	80.41 213	42.33 130	86.96 188	61.91 141	68.68 176	86.87 148

IterMVS-LS		66.63 208	65.36 206	70.42 236	75.10 252	48.90 213	81.45 224	76.69 267	61.05 128	55.71 246	77.10 245	45.86 88	83.65 251	57.44 185	57.88 258	78.70 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
bset_n11_16_dypcd		65.51 221	63.21 223	72.41 197	68.84 307	50.15 187	81.25 225	72.40 302	59.17 166	59.20 194	78.66 227	25.69 291	85.27 230	66.80 102	56.88 265	81.80 230

jajsoiax		63.21 234	60.84 239	70.32 238	68.33 313	44.45 278	81.23 226	81.05 186	53.37 251	50.96 280	77.81 235	17.49 330	85.49 226	59.31 163	58.05 253	81.02 250

HPM-MVS_fast		67.86 181	66.28 183	72.61 191	80.67 171	48.34 229	81.18 227	75.95 274	50.81 269	59.55 187	88.05 122	27.86 274	85.98 217	58.83 166	73.58 136	83.51 207

tfpnnormal		61.47 249	59.09 252	68.62 261	76.29 238	41.69 301	81.14 228	85.16 107	54.48 244	51.32 276	73.63 280	32.32 242	86.89 191	21.78 341	55.71 279	77.29 289

IS-MVSNet		68.80 164	67.55 161	72.54 193	78.50 207	43.43 287	81.03 229	79.35 217	59.12 169	57.27 232	86.71 140	46.05 83	87.70 171	44.32 262	75.60 121	86.49 157

eth_miper_zixun_eth		66.98 204	65.28 207	72.06 204	75.61 247	50.40 177	81.00 230	76.97 263	62.00 109	56.99 234	76.97 246	44.84 101	85.58 223	58.75 167	54.42 287	80.21 260

mvs_tets		62.96 237	60.55 241	70.19 239	68.22 316	44.24 282	80.90 231	80.74 190	52.99 254	50.82 282	77.56 236	16.74 333	85.44 227	59.04 165	57.94 255	80.89 251

tttt0517		68.33 173	66.29 182	74.46 150	78.08 212	49.06 207	80.88 232	89.08 23	54.40 245	54.75 251	80.77 211	51.31 38	90.33 94	49.35 235	58.01 254	83.99 197

FC-MVSNet-test		67.49 189	67.91 150	66.21 280	76.06 240	33.06 333	80.82 233	87.18 61	64.44 74	54.81 249	82.87 181	50.40 47	82.60 258	48.05 243	66.55 188	82.98 220

sss		70.49 133	70.13 123	71.58 219	81.59 147	39.02 314	80.78 234	84.71 120	59.34 157	66.61 104	88.09 120	37.17 192	85.52 224	61.82 143	71.02 160	90.20 83

HPM-MVS		72.60 103	71.50 104	75.89 127	82.02 136	51.42 159	80.70 235	83.05 156	56.12 224	64.03 140	89.53 93	37.55 183	88.37 147	70.48 83	80.04 86	87.88 131
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs6		59.64 256	57.15 262	67.09 271	66.01 320	36.86 323	80.50 236	78.64 230	45.05 304	49.05 287	73.94 275	27.28 278	86.10 211	43.96 264	49.94 304	78.31 278

IterMVS		63.77 231	61.67 231	70.08 242	72.68 278	51.24 164	80.44 237	75.51 276	60.51 138	51.41 275	73.70 279	32.08 245	78.91 288	54.30 204	54.35 288	80.08 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		59.12 261	58.81 255	60.08 313	70.68 299	45.07 272	80.42 238	74.25 286	43.54 315	50.02 283	73.73 276	31.97 246	56.74 346	51.06 226	53.60 293	78.42 276

ACMH+	54.58 15	58.55 271	55.24 274	68.50 263	74.68 258	45.80 266	80.27 239	70.21 317	47.15 287	42.77 314	75.48 267	16.73 334	85.98 217	35.10 299	54.78 285	73.72 316

Anonymous20231206		59.08 263	57.59 259	63.55 295	68.77 309	32.14 338	80.26 240	79.78 205	50.00 273	49.39 285	72.39 293	26.64 283	78.36 291	33.12 307	57.94 255	80.14 261

1314		71.11 123	69.41 131	76.22 118	79.32 186	50.49 174	80.23 241	85.14 109	59.44 153	58.93 198	88.89 106	33.83 230	89.60 114	61.49 144	77.42 104	88.57 120

1121		68.79 165	66.77 173	74.82 145	83.08 111	53.46 101	80.23 241	71.53 310	45.47 301	66.31 109	87.19 133	34.02 225	85.13 235	52.78 215	80.36 81	85.87 169

MVS		76.91 44	75.48 54	81.23 16	84.56 77	55.21 57	80.23 241	91.64 2	58.65 178	65.37 120	91.48 49	45.72 90	95.05 14	72.11 76	89.52 8	93.44 7

ACMH	53.70 16	59.78 255	55.94 272	71.28 222	76.59 233	48.35 228	80.15 244	76.11 272	49.74 274	41.91 317	73.45 283	16.50 335	90.31 95	31.42 312	57.63 261	75.17 307
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		63.34 233	61.07 238	70.16 240	70.14 300	50.53 173	79.97 245	71.41 312	55.08 235	54.12 257	78.58 228	32.79 238	82.09 262	50.33 228	57.22 263	77.86 283

abl_6		68.03 178	66.15 186	73.66 173	78.54 205	48.48 225	79.77 246	78.04 241	47.39 285	63.70 147	88.25 117	28.21 269	89.06 120	60.17 161	71.25 158	83.45 208

MVS_111021_LR		69.07 156	67.91 150	72.54 193	77.27 224	49.56 199	79.77 246	73.96 290	59.33 159	60.73 174	87.82 124	30.19 261	81.53 264	69.94 85	72.19 152	86.53 156

CNLPA		60.59 253	58.44 256	67.05 273	79.21 188	47.26 246	79.75 248	64.34 330	42.46 320	51.90 274	83.94 166	27.79 276	75.41 313	37.12 283	59.49 237	78.47 274

EI-MVSNet		69.70 149	68.70 140	72.68 190	75.00 254	48.90 213	79.54 249	87.16 62	61.05 128	63.88 145	83.74 170	45.87 87	90.44 90	57.42 186	64.68 201	78.70 270

CVMVSNet		60.85 252	60.44 243	62.07 301	75.00 254	32.73 335	79.54 249	73.49 295	36.98 332	56.28 243	83.74 170	29.28 266	69.53 337	46.48 252	63.23 213	83.94 202

AUN-MVS		68.20 177	66.35 180	73.76 170	76.37 234	47.45 243	79.52 251	79.52 211	60.98 130	62.34 160	86.02 147	36.59 204	86.94 189	62.32 137	53.47 295	86.89 147

PVSNet_0	57.04 13	61.19 250	57.24 261	73.02 184	77.45 222	50.31 184	79.43 252	77.36 255	63.96 81	47.51 297	72.45 292	25.03 295	83.78 249	52.76 218	19.22 352	84.96 184

PCF-MVS	61.03 10	70.10 138	68.40 144	75.22 140	77.15 229	51.99 143	79.30 253	82.12 167	56.47 221	61.88 166	86.48 145	43.98 109	87.24 181	55.37 197	72.79 146	86.43 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	52.38 18	60.89 251	58.97 254	66.68 278	81.77 141	45.70 267	78.96 254	74.04 289	43.66 314	47.63 294	83.19 180	23.52 303	77.78 303	37.47 280	60.46 231	76.55 298
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		63.49 232	61.39 235	69.77 246	69.29 305	48.93 212	78.89 255	77.71 248	60.64 137	49.70 284	72.10 298	27.08 280	83.48 253	54.48 203	62.65 220	76.90 291

OMC-MVS		65.97 218	65.06 210	68.71 259	72.97 274	42.58 297	78.61 256	75.35 279	54.72 240	59.31 191	86.25 146	33.30 233	77.88 300	57.99 176	67.05 185	85.66 173

PAPM_NR		71.80 116	69.98 125	77.26 97	81.54 150	53.34 110	78.60 257	85.25 104	53.46 249	60.53 177	88.66 109	45.69 91	89.24 118	56.49 190	79.62 91	89.19 104

mvs_anonymous		72.29 110	70.74 113	76.94 106	82.85 121	54.72 74	78.43 258	81.54 177	63.77 83	61.69 167	79.32 220	51.11 39	85.31 228	62.15 140	75.79 118	90.79 68

test222					79.36 184	50.97 166	77.99 259	67.84 322	42.54 319	62.84 156	86.53 143	30.26 260			76.91 109	85.23 180

v7n		62.50 242	59.27 251	72.20 201	67.25 319	49.83 194	77.87 260	80.12 198	52.50 257	48.80 288	73.07 284	32.10 244	87.90 163	46.83 251	54.92 283	78.86 268

test20.03		55.22 289	54.07 282	58.68 317	63.14 335	25.00 349	77.69 261	74.78 282	52.64 255	43.43 310	72.39 293	26.21 285	74.76 315	29.31 318	47.05 316	76.28 300

testdata1							77.55 262		64.14 77

PEN-MVS		58.35 273	57.15 262	61.94 303	67.55 318	34.39 327	77.01 263	78.35 237	51.87 262	47.72 293	76.73 252	33.91 227	73.75 321	34.03 302	47.17 314	77.68 285

WR-MVS_H		58.91 266	58.04 257	61.54 306	69.07 306	33.83 330	76.91 264	81.99 168	51.40 266	48.17 289	74.67 271	40.23 155	74.15 317	31.78 311	48.10 307	76.64 296

CP-MVSNet		58.54 272	57.57 260	61.46 307	68.50 311	33.96 329	76.90 265	78.60 233	51.67 265	47.83 292	76.60 254	34.99 219	72.79 326	35.45 292	47.58 310	77.64 287

PS-CasMVS		58.12 274	57.03 264	61.37 308	68.24 315	33.80 331	76.73 266	78.01 242	51.20 267	47.54 296	76.20 262	32.85 236	72.76 327	35.17 297	47.37 312	77.55 288

tpm		68.36 171	67.48 163	70.97 229	79.93 180	51.34 161	76.58 267	78.75 228	67.73 36	63.54 151	74.86 270	48.33 58	72.36 329	53.93 206	63.71 206	89.21 103

MVS_0304		56.72 278	55.17 275	61.37 308	70.71 296	36.80 324	75.74 268	68.75 321	44.11 312	52.53 268	68.20 315	15.05 339	74.53 316	42.98 268	58.44 246	72.79 323

DTE-MVSNet		57.03 277	55.73 273	60.95 312	65.94 321	32.57 336	75.71 269	77.09 259	51.16 268	46.65 301	76.34 257	32.84 237	73.22 325	30.94 315	44.87 322	77.06 290

tpmrst		71.04 124	69.77 127	74.86 144	83.19 107	55.86 44	75.64 270	78.73 229	67.88 34	64.99 127	73.73 276	49.96 50	79.56 286	65.92 110	67.85 182	89.14 106

CostFormer		73.89 86	72.30 91	78.66 57	82.36 134	56.58 29	75.56 271	85.30 100	66.06 55	70.50 81	76.88 250	57.02 12	89.06 120	68.27 95	68.74 175	90.33 79

HY-MVS	67.03 5	73.90 85	73.14 79	76.18 120	84.70 76	47.36 244	75.56 271	86.36 78	66.27 49	70.66 79	83.91 167	51.05 40	89.31 117	67.10 101	72.61 148	91.88 39

K. test v3		54.04 294	49.42 302	67.92 266	68.55 310	42.57 298	75.51 273	63.07 332	52.07 259	39.21 325	64.59 325	19.34 322	82.21 259	37.11 284	25.31 349	78.97 267

Vis-MVSNet (Re-imp)		65.52 220	65.63 198	65.17 288	77.49 221	30.54 340	75.49 274	77.73 247	59.34 157	52.26 272	86.69 141	49.38 54	80.53 274	37.07 285	75.28 123	84.42 190

pmmvs-eth3d		55.97 286	52.78 290	65.54 284	61.02 340	46.44 255	75.36 275	67.72 323	49.61 275	43.65 309	67.58 317	21.63 314	77.04 305	44.11 263	44.33 323	73.15 322

TAPA-MVS	56.12 14	61.82 248	60.18 245	66.71 276	78.48 208	37.97 319	75.19 276	76.41 271	46.82 290	57.04 233	86.52 144	27.67 277	77.03 306	26.50 332	67.02 186	85.14 181
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet5		58.61 269	56.45 266	65.10 289	77.20 228	39.74 311	74.77 277	77.12 258	50.27 272	43.28 312	67.71 316	26.15 286	76.90 308	36.78 288	54.78 285	78.65 272

SixPastTwentyTwo		54.37 291	50.10 298	67.21 270	70.70 297	41.46 304	74.73 278	64.69 328	47.56 284	39.12 326	69.49 309	18.49 327	84.69 242	31.87 310	34.20 342	75.48 304

F-COLMAP		55.96 287	53.65 285	62.87 299	72.76 277	42.77 294	74.70 279	70.37 316	40.03 323	41.11 321	79.36 219	17.77 329	73.70 322	32.80 308	53.96 290	72.15 324

MSDG		59.44 257	55.14 277	72.32 200	74.69 257	50.71 168	74.39 280	73.58 293	44.44 308	43.40 311	77.52 237	19.45 321	90.87 81	31.31 313	57.49 262	75.38 305

tpm2		70.82 128	68.44 143	77.98 77	80.78 167	56.11 40	74.21 281	81.28 184	60.24 141	68.04 93	75.27 268	52.26 32	88.50 144	55.82 196	68.03 179	89.33 98

UniMVSNet_ETH3D		62.51 241	60.49 242	68.57 262	68.30 314	40.88 309	73.89 282	79.93 202	51.81 264	54.77 250	79.61 217	24.80 296	81.10 267	49.93 230	61.35 227	83.73 205

UA-Net		67.32 195	66.23 184	70.59 233	78.85 196	41.23 306	73.60 283	75.45 278	61.54 120	66.61 104	84.53 161	38.73 169	86.57 201	42.48 272	74.24 131	83.98 199

Anonymous20240521		51.65 303	48.42 304	61.34 310	56.43 346	39.65 313	73.57 284	73.47 298	36.64 334	36.59 331	63.98 326	10.75 345	72.25 330	35.35 293	49.01 305	72.11 325

ab-mvs		70.65 131	69.11 137	75.29 137	80.87 166	46.23 261	73.48 285	85.24 105	59.99 144	66.65 102	80.94 209	43.13 126	88.69 135	63.58 130	68.07 178	90.95 66

LS3D		56.40 283	53.82 283	64.12 292	81.12 160	45.69 268	73.42 286	66.14 325	35.30 340	43.24 313	79.88 215	22.18 311	79.62 285	19.10 347	64.00 204	67.05 336

testmvs		6.14 333	8.18 336	0.01 345	0.01 366	0.00 368	73.40 287	0.00 367	0.00 362	0.02 363	0.15 363	0.00 368	0.00 363	0.02 361	0.00 361	0.02 359

UnsupCasMVSNet_eth		57.56 275	55.15 276	64.79 291	64.57 330	33.12 332	73.17 288	83.87 140	58.98 173	41.75 318	70.03 308	22.54 307	79.92 281	46.12 256	35.31 337	81.32 246

anonymousdsp		60.46 254	57.65 258	68.88 253	63.63 333	45.09 271	72.93 289	78.63 231	46.52 292	51.12 277	72.80 288	21.46 315	83.07 257	57.79 181	53.97 289	78.47 274

EU-MVSNet		52.63 300	50.72 296	58.37 318	62.69 337	28.13 347	72.60 290	75.97 273	30.94 342	40.76 323	72.11 297	20.16 319	70.80 333	35.11 298	46.11 319	76.19 301

dp		64.41 225	61.58 232	72.90 186	82.40 132	54.09 88	72.53 291	76.59 269	60.39 139	55.68 247	70.39 307	35.18 217	76.90 308	39.34 277	61.71 226	87.73 135

N_pmnet		41.25 315	39.77 318	45.66 331	68.50 311	0.82 366	72.51 292	0.38 366	35.61 337	35.26 336	61.51 331	20.07 320	67.74 338	23.51 338	40.63 330	68.42 335

MDTV_nov1_ep13			61.56 233		81.68 142	55.12 60	72.41 293	78.18 239	59.19 162	58.85 202	69.29 311	34.69 220	86.16 208	36.76 289	62.96 218

YYNet1		53.82 296	49.96 299	65.41 286	70.09 302	48.95 210	72.30 294	71.66 308	44.25 310	31.89 344	63.07 329	23.73 301	73.95 319	33.26 305	39.40 333	73.34 319

MDA-MVSNet_test_wron		53.82 296	49.95 300	65.43 285	70.13 301	49.05 208	72.30 294	71.65 309	44.23 311	31.85 345	63.13 328	23.68 302	74.01 318	33.25 306	39.35 334	73.23 321

testgi		54.25 293	52.57 292	59.29 315	62.76 336	21.65 354	72.21 296	70.47 315	53.25 252	41.94 316	77.33 241	14.28 340	77.95 299	29.18 319	51.72 300	78.28 279

KD-MVS_2432*1600		59.04 264	56.44 267	66.86 274	79.07 190	45.87 264	72.13 297	80.42 195	55.03 236	48.15 290	71.01 301	36.73 199	78.05 296	35.21 295	30.18 347	76.67 293

miper_refine_blended		59.04 264	56.44 267	66.86 274	79.07 190	45.87 264	72.13 297	80.42 195	55.03 236	48.15 290	71.01 301	36.73 199	78.05 296	35.21 295	30.18 347	76.67 293

PatchmatchNet		67.07 202	63.63 222	77.40 90	83.10 108	58.03 11	72.11 299	77.77 246	58.85 175	59.37 189	70.83 303	37.84 175	84.93 239	42.96 269	69.83 169	89.26 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test123		6.01 334	8.01 337	0.01 345	0.00 367	0.01 367	71.93 300	0.00 367	0.00 362	0.02 363	0.11 364	0.00 368	0.00 363	0.02 361	0.00 361	0.02 359

EPMVS		68.45 170	65.44 204	77.47 89	84.91 73	56.17 39	71.89 301	81.91 172	61.72 116	60.85 172	72.49 290	36.21 206	87.06 185	47.32 247	71.62 155	89.17 105

UnsupCasMVSNet_bld		53.86 295	50.53 297	63.84 293	63.52 334	34.75 326	71.38 302	81.92 171	46.53 291	38.95 327	57.93 340	20.55 318	80.20 279	39.91 276	34.09 343	76.57 297

COLMAP_ROB	43.60 20	50.90 305	48.05 306	59.47 314	67.81 317	40.57 310	71.25 303	62.72 334	36.49 335	36.19 333	73.51 281	13.48 341	73.92 320	20.71 343	50.26 303	63.92 342
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MDTV_nov1_ep13_2view						43.62 286	71.13 304		54.95 238	59.29 192		36.76 198		46.33 254		87.32 142

test_post1							70.84 305				14.72 360	34.33 223	83.86 246	48.80 237

new-patchmatchnet		48.21 309	46.55 311	53.18 325	57.73 344	18.19 358	70.24 306	71.02 314	45.70 298	33.70 339	60.23 334	18.00 328	69.86 336	27.97 326	34.35 340	71.49 330

pmmvs3		45.53 314	41.55 317	57.44 320	48.97 352	39.68 312	70.06 307	57.66 338	28.32 345	34.06 338	57.29 341	8.50 350	66.85 339	34.86 300	34.26 341	65.80 339

tpmvs		62.45 244	59.42 249	71.53 220	83.93 90	54.32 82	70.03 308	77.61 249	51.91 261	53.48 264	68.29 314	37.91 174	86.66 196	33.36 304	58.27 248	73.62 317

tpm cat1		66.28 213	62.78 224	76.77 111	81.40 156	57.14 22	70.03 308	77.19 256	53.00 253	58.76 204	70.73 306	46.17 80	86.73 194	43.27 266	64.46 202	86.44 158

PatchMatch-RL		56.66 279	53.75 284	65.37 287	77.91 217	45.28 270	69.78 310	60.38 335	41.35 321	47.57 295	73.73 276	16.83 332	76.91 307	36.99 286	59.21 240	73.92 315

MDA-MVSNet-bldmvs		51.56 304	47.75 309	63.00 298	71.60 289	47.32 245	69.70 311	72.12 304	43.81 313	27.65 349	63.38 327	21.97 313	75.96 310	27.30 329	32.19 344	65.70 340

miper_lstm_enhance		63.91 228	62.30 227	68.75 258	75.06 253	46.78 250	69.02 312	81.14 185	59.68 149	52.76 267	72.39 293	40.71 150	77.99 298	56.81 189	53.09 296	81.48 238

GG-mvs-BLEND				77.77 81	86.68 42	50.61 170	68.67 313	88.45 44		68.73 87	87.45 131	59.15 8	90.67 84	54.83 199	87.67 15	92.03 35

OurMVSNet-221017-0		52.39 301	48.73 303	63.35 297	65.21 326	38.42 317	68.54 314	64.95 327	38.19 327	39.57 324	71.43 300	13.23 342	79.92 281	37.16 282	40.32 332	71.72 327

MIMVSNet1		50.35 306	47.81 307	57.96 319	61.53 339	27.80 348	67.40 315	74.06 288	43.25 316	33.31 343	65.38 324	16.03 336	71.34 331	21.80 340	47.55 311	74.75 309

MTAPA		72.73 101	71.22 109	77.27 95	81.54 150	53.57 97	67.06 316	81.31 181	59.41 154	68.39 90	90.96 58	36.07 208	89.01 124	73.80 64	82.45 61	89.23 101

MIMVSNet		63.12 235	60.29 244	71.61 216	75.92 244	46.65 252	65.15 317	81.94 169	59.14 167	54.65 252	69.47 310	25.74 288	80.63 272	41.03 274	69.56 172	87.55 137

XVG-OURS-SEG-HR		62.02 246	59.54 248	69.46 249	65.30 325	45.88 263	65.06 318	73.57 294	46.45 293	57.42 230	83.35 177	26.95 281	78.09 294	53.77 207	64.03 203	84.42 190

XVG-OURS		61.88 247	59.34 250	69.49 248	65.37 324	46.27 259	64.80 319	73.49 295	47.04 288	57.41 231	82.85 182	25.15 294	78.18 292	53.00 212	64.98 196	84.01 196

gg-mvs-nofinetune		67.43 191	64.53 215	76.13 121	85.95 46	47.79 240	64.38 320	88.28 45	39.34 324	66.62 103	41.27 346	58.69 11	89.00 126	49.64 233	86.62 27	91.59 44

XVG-ACMP-BASELINE		56.03 285	52.85 289	65.58 283	61.91 338	40.95 308	63.36 321	72.43 301	45.20 303	46.02 303	74.09 273	9.20 349	78.12 293	45.13 258	58.27 248	77.66 286

TinyColmap		48.15 310	44.49 314	59.13 316	65.73 323	38.04 318	63.34 322	62.86 333	38.78 325	29.48 347	67.23 319	6.46 354	73.30 324	24.59 335	41.90 329	66.04 338

MVS-HIRNet		49.01 308	44.71 312	61.92 304	76.06 240	46.61 253	63.23 323	54.90 341	24.77 347	33.56 340	36.60 349	21.28 316	75.88 311	29.49 317	62.54 221	63.26 344

PM-MVS		46.92 312	43.76 316	56.41 322	52.18 349	32.26 337	63.21 324	38.18 353	37.99 329	40.78 322	66.20 320	5.09 357	65.42 340	48.19 242	41.99 328	71.54 329

AllTest		47.32 311	44.66 313	55.32 323	65.08 327	37.50 321	62.96 325	54.25 343	35.45 338	33.42 341	72.82 286	9.98 346	59.33 343	24.13 336	43.84 324	69.13 332

USDC		54.36 292	51.23 295	63.76 294	64.29 331	37.71 320	62.84 326	73.48 297	56.85 210	35.47 335	71.94 299	9.23 348	78.43 290	38.43 279	48.57 306	75.13 308

Patchmatch-RL test		58.72 268	54.32 280	71.92 213	63.91 332	44.25 281	61.73 327	55.19 340	57.38 204	49.31 286	54.24 343	37.60 182	80.89 269	62.19 139	47.28 313	90.63 71

SCA		63.84 229	60.01 246	75.32 136	78.58 204	57.92 12	61.61 328	77.53 250	56.71 215	57.75 221	70.77 304	31.97 246	79.91 283	48.80 237	56.36 267	88.13 127

CMPMVS	40.41 21	55.34 288	52.64 291	63.46 296	60.88 341	43.84 284	61.58 329	71.06 313	30.43 343	36.33 332	74.63 272	24.14 299	75.44 312	48.05 243	66.62 187	71.12 331
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		58.82 267	56.54 265	65.68 282	79.31 187	29.09 345	61.39 330	45.79 348	60.73 135	37.65 330	72.47 291	31.42 252	81.08 268	49.66 232	70.41 164	86.87 148

CR-MVSNet		62.47 243	59.04 253	72.77 189	73.97 266	56.57 30	60.52 331	71.72 306	60.04 142	57.49 227	65.86 321	38.94 166	80.31 276	42.86 270	59.93 233	81.42 239

RPMNet		59.29 258	54.25 281	74.42 152	73.97 266	56.57 30	60.52 331	76.98 260	35.72 336	57.49 227	58.87 339	37.73 179	85.26 231	27.01 330	59.93 233	81.42 239

Patchmtry		56.56 281	52.95 288	67.42 269	72.53 280	50.59 172	59.05 333	71.72 306	37.86 330	46.92 298	65.86 321	38.94 166	80.06 280	36.94 287	46.72 318	71.60 328

TDRefinement		40.91 316	38.37 319	48.55 329	50.45 350	33.03 334	58.98 334	50.97 346	28.50 344	29.89 346	67.39 318	6.21 356	54.51 347	17.67 348	35.25 338	58.11 345

DIV-MVS_2432*1600		49.24 307	46.85 310	56.44 321	54.32 347	22.87 351	57.39 335	73.36 299	44.36 309	37.98 329	59.30 338	18.97 324	71.17 332	33.48 303	42.44 327	75.26 306

PatchT		56.60 280	52.97 287	67.48 268	72.94 275	46.16 262	57.30 336	73.78 291	38.77 326	54.37 255	57.26 342	37.52 184	78.06 295	32.02 309	52.79 297	78.23 281

ANet_high		34.39 319	29.59 324	48.78 328	30.34 360	22.28 352	55.53 337	63.79 331	38.11 328	15.47 353	36.56 350	6.94 351	59.98 342	13.93 351	5.64 360	64.08 341

ADS-MVSNet2		55.21 290	51.44 294	66.51 279	80.60 172	49.56 199	55.03 338	65.44 326	44.72 305	51.00 278	61.19 332	22.83 304	75.41 313	28.54 323	53.63 291	74.57 311

ADS-MVSNet		56.17 284	51.95 293	68.84 254	80.60 172	53.07 120	55.03 338	70.02 318	44.72 305	51.00 278	61.19 332	22.83 304	78.88 289	28.54 323	53.63 291	74.57 311

RPSCF		45.77 313	44.13 315	50.68 327	57.67 345	29.66 341	54.92 340	45.25 350	26.69 346	45.92 304	75.92 265	17.43 331	45.70 354	27.44 328	45.95 320	76.67 293

new_pmnet		33.56 320	31.89 323	38.59 334	49.01 351	20.42 355	51.01 341	37.92 354	20.58 348	23.45 350	46.79 345	6.66 353	49.28 352	20.00 346	31.57 346	46.09 350

E-PMN		19.16 326	18.40 330	21.44 340	36.19 358	13.63 361	47.59 342	30.89 358	10.73 356	5.91 360	16.59 356	3.66 360	39.77 356	5.95 357	8.14 355	10.92 355

EMVS		18.42 327	17.66 331	20.71 341	34.13 359	12.64 362	46.94 343	29.94 359	10.46 358	5.58 361	14.93 359	4.23 359	38.83 357	5.24 359	7.51 357	10.67 356

CHOSEN 280x420		57.53 276	56.38 269	60.97 311	74.01 264	48.10 236	46.30 344	54.31 342	48.18 282	50.88 281	77.43 240	38.37 172	59.16 345	54.83 199	63.14 216	75.66 303

LTVRE_ROB	45.45 19	52.73 299	49.74 301	61.69 305	69.78 303	34.99 325	44.52 345	67.60 324	43.11 317	43.79 308	74.03 274	18.54 326	81.45 265	28.39 325	57.94 255	68.62 334
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
LF4IMVS		33.04 321	32.55 322	34.52 337	40.96 355	22.03 353	44.45 346	35.62 356	20.42 349	28.12 348	62.35 330	5.03 358	31.88 359	21.61 342	34.42 339	49.63 348

Patchmatch-test		53.33 298	48.17 305	68.81 256	73.31 269	42.38 299	42.98 347	58.23 337	32.53 341	38.79 328	70.77 304	39.66 162	73.51 323	25.18 334	52.06 299	90.55 72

PMMVS2		26.71 324	22.98 328	37.87 335	36.89 357	8.51 364	42.51 348	29.32 360	19.09 352	13.01 354	37.54 347	2.23 361	53.11 348	14.54 350	11.71 353	51.99 347

FPMVS		35.40 318	33.67 321	40.57 333	46.34 354	28.74 346	41.05 349	57.05 339	20.37 350	22.27 351	53.38 344	6.87 352	44.94 355	8.62 353	47.11 315	48.01 349

PMVS	19.57 22	25.07 325	22.43 329	32.99 338	23.12 363	22.98 350	40.98 350	35.19 357	15.99 353	11.95 356	35.87 351	1.47 365	49.29 351	5.41 358	31.90 345	26.70 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
JIA-IIPM		52.33 302	47.77 308	66.03 281	71.20 294	46.92 249	40.00 351	76.48 270	37.10 331	46.73 299	37.02 348	32.96 235	77.88 300	35.97 290	52.45 298	73.29 320

DSMNet-mixed		38.35 317	35.36 320	47.33 330	48.11 353	14.91 360	37.87 352	36.60 355	19.18 351	34.37 337	59.56 337	15.53 337	53.01 349	20.14 345	46.89 317	74.07 313

ambc				62.06 302	53.98 348	29.38 343	35.08 353	79.65 209		41.37 319	59.96 335	6.27 355	82.15 260	35.34 294	38.22 335	74.65 310

LCM-MVSNet		28.07 322	23.85 327	40.71 332	27.46 362	18.93 357	30.82 354	46.19 347	12.76 355	16.40 352	34.70 352	1.90 363	48.69 353	20.25 344	24.22 350	54.51 346

wuyk23d		9.11 331	8.77 335	10.15 343	40.18 356	16.76 359	20.28 355	1.01 365	2.58 360	2.66 362	0.98 362	0.23 367	12.49 361	4.08 360	6.90 358	1.19 358

MVE	16.60 23	17.34 329	13.39 332	29.16 339	28.43 361	19.72 356	13.73 356	23.63 361	7.23 359	7.96 358	21.41 354	0.80 366	36.08 358	6.97 355	10.39 354	31.69 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		27.47 323	24.26 326	37.12 336	60.55 342	29.17 344	11.68 357	60.00 336	14.18 354	10.52 357	15.12 358	2.20 362	63.01 341	8.39 354	35.65 336	19.18 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		9.44 330	10.68 333	5.73 344	2.49 365	4.21 365	10.48 358	18.04 362	0.34 361	12.59 355	20.49 355	11.39 343	7.03 362	13.84 352	6.46 359	5.95 357

uanet_test		0.00 336	0.00 339	0.00 347	0.00 367	0.00 368	0.00 359	0.00 367	0.00 362	0.00 365	0.00 365	0.00 368	0.00 363	0.00 363	0.00 361	0.00 361

cdsmvs_eth3d_5k		18.33 328	24.44 325	0.00 347	0.00 367	0.00 368	0.00 359	89.40 17	0.00 362	0.00 365	92.02 36	38.55 170	0.00 363	0.00 363	0.00 361	0.00 361

pcd_1.5k_mvsjas		3.15 335	4.20 338	0.00 347	0.00 367	0.00 368	0.00 359	0.00 367	0.00 362	0.00 365	0.00 365	37.77 176	0.00 363	0.00 363	0.00 361	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 367	0.00 368	0.00 359	0.00 367	0.00 362	0.00 365	0.00 365	0.00 368	0.00 363	0.00 363	0.00 361	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 367	0.00 368	0.00 359	0.00 367	0.00 362	0.00 365	0.00 365	0.00 368	0.00 363	0.00 363	0.00 361	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 367	0.00 368	0.00 359	0.00 367	0.00 362	0.00 365	0.00 365	0.00 368	0.00 363	0.00 363	0.00 361	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 367	0.00 368	0.00 359	0.00 367	0.00 362	0.00 365	0.00 365	0.00 368	0.00 363	0.00 363	0.00 361	0.00 361

ab-mvs-re		7.68 332	10.24 334	0.00 347	0.00 367	0.00 368	0.00 359	0.00 367	0.00 362	0.00 365	92.12 33	0.00 368	0.00 363	0.00 363	0.00 361	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 367	0.00 368	0.00 359	0.00 367	0.00 362	0.00 365	0.00 365	0.00 368	0.00 363	0.00 363	0.00 361	0.00 361

ZD-MVS					89.55 11	53.46 101		84.38 125	57.02 208	73.97 43	91.03 54	44.57 105	91.17 72	75.41 53	81.78 69

IU-MVS					89.48 14	57.49 17		91.38 5	66.22 50	88.26 1				82.83 7	87.60 16	92.44 25

test_241102_TWO								88.76 34	57.50 202	83.60 5	94.09 4	56.14 16	96.37 5	82.28 11	87.43 18	92.55 23

test_241102_ONE					89.48 14	56.89 26		88.94 27	57.53 200	84.61 3	93.29 13	58.81 9	96.45 1

test_0728_THIRD									58.00 188	81.91 9	93.64 10	56.54 13	96.44 2	81.64 16	86.86 23	92.23 29

GSMVS																88.13 127

test_part2					89.33 19	55.48 48				82.27 8

sam_mvs1												38.86 168				88.13 127

sam_mvs												35.99 212

MTGPA								81.31 181

test_post											16.22 357	37.52 184	84.72 241

patchmatchnet-post											59.74 336	38.41 171	79.91 283

gm-plane-assit					83.24 106	54.21 85			70.91 12		88.23 118		95.25 12	66.37 106

test9_res														78.72 28	85.44 40	91.39 52

agg_prior2														75.65 48	85.11 44	91.01 63

agg_prior					85.64 54	54.92 67		83.61 145		72.53 60			88.10 159

TestCases				55.32 323	65.08 327	37.50 321		54.25 343	35.45 338	33.42 341	72.82 286	9.98 346	59.33 343	24.13 336	43.84 324	69.13 332

test_prior				78.39 66	86.35 44	54.91 69		85.45 92					89.70 110			90.55 72

新几何1				73.30 182	83.10 108	53.48 100		71.43 311	45.55 299	66.14 110	87.17 134	33.88 229	80.54 273	48.50 240	80.33 82	85.88 168

旧先验1					81.57 149	47.48 242		71.83 305			88.66 109	36.94 195			78.34 99	88.67 116

原ACMM1				76.13 121	84.89 74	54.59 79		85.26 103	51.98 260	66.70 101	87.07 136	40.15 157	89.70 110	51.23 224	85.06 45	84.10 193

testdata2													77.81 302	45.64 257

segment_acmp												44.97 99

testdata				67.08 272	77.59 219	45.46 269		69.20 320	44.47 307	71.50 73	88.34 114	31.21 253	70.76 334	52.20 221	75.88 117	85.03 182

test12				79.24 36	86.89 40	56.08 41		85.16 107		72.27 65		47.15 71	91.10 74		85.93 33	90.54 75

plane_prior7					77.95 214	48.46 227

plane_prior6					78.42 209	49.39 203						36.04 210

plane_prior5								82.59 162					88.30 152	65.46 116	72.34 150	84.49 188

plane_prior4											83.28 178

plane_prior3						48.95 210			64.01 78	62.15 162

plane_prior1					78.31 211

n2								0.00 367

nn								0.00 367

door-mid								41.31 352

lessismore_v0				67.98 265	64.76 329	41.25 305		45.75 349		36.03 334	65.63 323	19.29 323	84.11 245	35.67 291	21.24 351	78.59 273

LGP-MVS_train				72.02 205	74.42 260	48.60 219		80.64 191	54.69 241	53.75 261	83.83 168	25.73 289	86.98 186	60.33 159	64.71 198	80.48 256

test11								84.25 130

door								43.27 351

HQP5-MVS						51.56 154

BP-MVS														66.70 103

HQP4-MVS										64.47 136			88.61 138			84.91 185

HQP3-MVS								83.68 142							73.12 141

HQP2-MVS												37.35 187

NP-MVS					78.76 197	50.43 176					85.12 157

ACMMP++_ref															63.20 214

ACMMP++															59.38 238

Test By Simon												39.38 163

ITE_SJBPF				51.84 326	58.03 343	31.94 339		53.57 345	36.67 333	41.32 320	75.23 269	11.17 344	51.57 350	25.81 333	48.04 308	72.02 326

DeepMVS_CX				13.10 342	21.34 364	8.99 363		10.02 364	10.59 357	7.53 359	30.55 353	1.82 364	14.55 360	6.83 356	7.52 356	15.75 354

High-resolution multi-view benchmark